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I'm still exploring what I can do with Jev but I made this little app which does the following - talk about your day (or anything really) - audio gets transcribed to text - text is parsed for cues across 12 colours linked to an emotion [a logic I... fed into this app for Jev to use] - a painting is drawn with code in JavaScript using the colours Jev thinks suits the transcript cues well - canvas has an underlying MIDI [utilises Tone.js] - watch your words turn into a painting which turns into a melody. App uses TypeSafe AI for Jev's colour + emotion inferences, Google Gemini for audio transcription, Claude for the JavaScript painting code, ChatGPT for front end UI refinement.show more

Soumya

4,939 subscribers

11,247 views • 14 days ago •via X (Twitter)

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23 Comments

Soumya14 days ago

using all my subscriptions at one place with this app 🤪

Vamsi Batchu14 days ago

this is so damn cool !

Soumya14 days ago

Thanks Vamsi! Haha :)

siddy14 days ago

words → colours → painting → music 😭 this is such a beautiful use of Jev

Soumya14 days ago

Thanks!

micah rich14 days ago

Holy crow I love this what

Disha Shah13 days ago

this is such a cool idea, soumya! Love it!

siddharth14 days ago

fantastic!

Nihal Srivastava14 days ago

This feels like more alive than a boring journaling. have you tried feeding the exact same story twice to see how consistent Jev’s colour interpretation is?

Soumya14 days ago

I did actually. My audio note was repeated once more because I kept taking pauses. Worked for me. But I will keep testing it because this is a huge issue with LLMs producing different results each time

Nihal Srivastava14 days ago

Hmmm maybe having a fixed mapping between emotions and colours could help.. Let Jev figure out the emotion, but keep the emotion <> colour mapping deterministic idk

Soumya14 days ago

It is exactly doing that. I also mentioned that it is not repeating results, but I highlighted a risk with LLMs hallucinating.

Nihal Srivastava14 days ago

Makes sense

Markiian14 days ago

words to a painting to a melody, so much fun

Aanand Madhav14 days ago

I can image an entire orchestra being played from one day of my emotional turmoils 😂😂

Shreyash Siddharth14 days ago

@amuldotexe so the music repeats after 12 ! permutations ?

Soumya14 days ago

@amuldotexe It will never repeat because it is taking cues from your audio input

Josh Madeiros13 days ago

What about this?

চৈতালি পাঠক14 days ago

Have you looked into mapping this into Hawkins' levels of consciousness?

Josh Madeiros14 days ago

Explore here!

Brjan | AI Builder14 days ago

transcribing audio is a solid start, but parsing emotions adds real depth

Himanshu Gupta14 days ago

great use of juvenile soumya

Deepak Sethi 🇮🇳 ॐ14 days ago

UPI पर MDR चार्ज का असर व्यापारियों और ग्राहकों पर क्या हो सकता है? इस विषय एक छोटा सा इंटरव्यू जरूर देखिए और अपनी राय साझा कीजिए। 🙏 👇 पूरी बात यहाँ 🙏👆

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81,882 views • 1 year ago

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unicode

20,745 views • 12 days ago

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NO1ennn

41,408 views • 9 days ago

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Ben Dicken

40,810 views • 16 days ago

this is f*cking gold 20 GitHub repos with 500K+ combined stars that will level up your JEV workflow AGENTS > jev-ultrafast: Jev picks every click and DOM target, a small LLM only types > hermes-jev-skills: routing, memory, compaction and skill picks in one pack > typesafe-computer-use: OCR reads your Mac screen, Jev picks the next click MEMORY > fast-jev-compaction: scores every tool call keep, truncate or drop instead of summarizing > jevmem: project memory for Claude Code, Cursor and Codex, updated every turn > jev-second-brain: your Obsidian vault, Jev judges which notes duplicate, revise or contradict SAFETY > jev-guard: a gate before every tool call, Jev scores the risk, you set allow, ask or deny TOOLS > skills: the official TypeSafe skill for Claude Code and Codex > system-one-adapter-python: dry-run your questions on an ordinary LLM before you burn a Jev key > jev-mcp: claim checks, screening and ranking as MCP tools > typesafe-mcp: plug Jev into any MCP client > json-render: Vercel's generative UI, where Jev picks the components OPEN MODELS > SemIf-OpenJev: semantic ifs from frozen open models kev: Jev-like models on Qwen that run on your MacBook > laya-mlx: the Laya decision engine on Apple silicon > clm: an open System One model with Choice, Noul and Score > jevlike: train your own Jev-like model TRADING > jev-trader: one buy or sell decision per Monad block START HERE > awesome-jev: the biggest map of everything built on Jev > awesome-jev-by-typesafe: use cases, patterns and starter code bookmark it before your next build

this is f*cking gold 20 GitHub repos with 500K+ combined stars that will level up your JEV workflow AGENTS > jev-ultrafast: Jev picks every click and DOM target, a small LLM only types > hermes-jev-skills: routing, memory, compaction and skill picks in one pack > typesafe-computer-use: OCR reads your Mac screen, Jev picks the next click MEMORY > fast-jev-compaction: scores every tool call keep, truncate or drop instead of summarizing > jevmem: project memory for Claude Code, Cursor and Codex, updated every turn > jev-second-brain: your Obsidian vault, Jev judges which notes duplicate, revise or contradict SAFETY > jev-guard: a gate before every tool call, Jev scores the risk, you set allow, ask or deny TOOLS > skills: the official TypeSafe skill for Claude Code and Codex > system-one-adapter-python: dry-run your questions on an ordinary LLM before you burn a Jev key > jev-mcp: claim checks, screening and ranking as MCP tools > typesafe-mcp: plug Jev into any MCP client > json-render: Vercel's generative UI, where Jev picks the components OPEN MODELS > SemIf-OpenJev: semantic ifs from frozen open models kev: Jev-like models on Qwen that run on your MacBook > laya-mlx: the Laya decision engine on Apple silicon > clm: an open System One model with Choice, Noul and Score > jevlike: train your own Jev-like model TRADING > jev-trader: one buy or sell decision per Monad block START HERE > awesome-jev: the biggest map of everything built on Jev > awesome-jev-by-typesafe: use cases, patterns and starter code bookmark it before your next build

NO1ennn

18,445 views • 7 days ago

I built an app with Claude Opus 4.5, then animated it with this new tool I found... This new iOS design workflow let me animate a character for my onboarding. Here it is: 1. build the app using Claude Code within Vibecode 2. entered image of my mascot into 3. copied animation code from there back into vibecode

I built an app with Claude Opus 4.5, then animated it with this new tool I found... This new iOS design workflow let me animate a character for my onboarding. Here it is: 1. build the app using Claude Code within Vibecode 2. entered image of my mascot into 3. copied animation code from there back into vibecode

Emily Lambert

292,874 views • 9 months ago

Jev has been exploding in popularity recently. If you already have access to the Jev API but aren't sure how to start experimenting with it, just copy this checklist: 1. jev-ultrafast Browser Use's fastest agent. Jev decides the next action and which element to click, and a language model is only called when text has to be typed. 2. typesafe-mario Jev plays Super Mario Bros. from structured emulator state, choosing every action from features pulled out of the game. 3. jev-plays-pokemon Reads Pokémon Red's game state as text, answers typed questions each turn, and lets plain code turn the answers into moves. 4. jev-drone A camera-only autonomous drone in MuJoCo with a Jev judgment model sitting in the control loop at 2.5 Hz. 5. robo-harness A real SO-101 robot arm workbench where Jev picks bounded joint steps from typed candidate actions under a spend budget. 6. fast-jev-compaction Claude Code plugin that replaces the compaction summary with Jev decisions, scoring every tool call for whether it is still needed. 7. jev-claude Routes Claude Code's own judgment calls through Jev: typed choices with probabilities at plan approval, on questions, and before risky commands. 8. is-malicious Supply-chain check before you run anything: Jev Noul checks over source and build files, returning the implicated files and lines. 9. sqlite-jev Jev inside SQL. Noul, Choice and Score judgments exposed as SQLite functions, with confidence on every row. 10. jevinci Paints images by having Jev predict every pixel's colour in parallel, with confidence deciding how wide each stroke is drawn. Copy these complete Jev blueprints - then read full Jev setup below ↓ ↓

Jev has been exploding in popularity recently. If you already have access to the Jev API but aren't sure how to start experimenting with it, just copy this checklist: 1. jev-ultrafast Browser Use's fastest agent. Jev decides the next action and which element to click, and a language model is only called when text has to be typed. 2. typesafe-mario Jev plays Super Mario Bros. from structured emulator state, choosing every action from features pulled out of the game. 3. jev-plays-pokemon Reads Pokémon Red's game state as text, answers typed questions each turn, and lets plain code turn the answers into moves. 4. jev-drone A camera-only autonomous drone in MuJoCo with a Jev judgment model sitting in the control loop at 2.5 Hz. 5. robo-harness A real SO-101 robot arm workbench where Jev picks bounded joint steps from typed candidate actions under a spend budget. 6. fast-jev-compaction Claude Code plugin that replaces the compaction summary with Jev decisions, scoring every tool call for whether it is still needed. 7. jev-claude Routes Claude Code's own judgment calls through Jev: typed choices with probabilities at plan approval, on questions, and before risky commands. 8. is-malicious Supply-chain check before you run anything: Jev Noul checks over source and build files, returning the implicated files and lines. 9. sqlite-jev Jev inside SQL. Noul, Choice and Score judgments exposed as SQLite functions, with confidence on every row. 10. jevinci Paints images by having Jev predict every pixel's colour in parallel, with confidence deciding how wide each stroke is drawn. Copy these complete Jev blueprints - then read full Jev setup below ↓ ↓

Hanako

29,894 views • 11 days ago

Audio Transcription with Google Gemini 1.5 Flash In this video, Gemini-Flash was able to transcribe 13 minutes of audio in 50-60 seconds. I have tested this with multiple audio files, and the transcription accuracy is close to 99%. If the audio is clear, you get 100% correct transcription. Even if the audio is really bad with a lot of noise, you still get around 95-96% accuracy. The code for this is available on GitHub. If you're interested, you can download and run it. You just need a Google API key.

Audio Transcription with Google Gemini 1.5 Flash In this video, Gemini-Flash was able to transcribe 13 minutes of audio in 50-60 seconds. I have tested this with multiple audio files, and the transcription accuracy is close to 99%. If the audio is clear, you get 100% correct transcription. Even if the audio is really bad with a lot of noise, you still get around 95-96% accuracy. The code for this is available on GitHub. If you're interested, you can download and run it. You just need a Google API key.

AshutoshShrivastava

123,061 views • 2 years ago

Google just shipped a free dictation app for Mac and iPhone called AI Edge Eloquent, and the model running it is Gemma 4 12B, entirely on your device. What it actually does: you speak, it transcribes, removes filler words, and polishes the result before dropping clean text into whatever app you are using. You can also give it voice commands to reformat, like "translate to Hindi" or "make this a bullet list." Audio and video file transcription is built in too. The interesting part is the privacy angle. Most AI writing tools send your audio to a server. This one never leaves your machine, which matters if you are dictating anything sensitive. The tradeoff is hardware: you need a Mac with at least 16GB of RAM to run a 12B parameter model comfortably.

Google just shipped a free dictation app for Mac and iPhone called AI Edge Eloquent, and the model running it is Gemma 4 12B, entirely on your device. What it actually does: you speak, it transcribes, removes filler words, and polishes the result before dropping clean text into whatever app you are using. You can also give it voice commands to reformat, like "translate to Hindi" or "make this a bullet list." Audio and video file transcription is built in too. The interesting part is the privacy angle. Most AI writing tools send your audio to a server. This one never leaves your machine, which matters if you are dictating anything sensitive. The tradeoff is hardware: you need a Mac with at least 16GB of RAM to run a 12B parameter model comfortably.

Aditya ⚡Rao

85,131 views • 3 months ago

Claude Code with Sonnet 4.5 is actually incredible I gave it a prompt for a super complex app, and it one shot the entire thing In this video I walk you through how to use Claude Code to build a prompt library app you can start using immediately (no coding experience required):

Claude Code with Sonnet 4.5 is actually incredible I gave it a prompt for a super complex app, and it one shot the entire thing In this video I walk you through how to use Claude Code to build a prompt library app you can start using immediately (no coding experience required):

Alex Finn

40,955 views • 1 year ago

How to convert ANY web app into a Mobile App... That can be shipped to the App Store from your phone. (Zero Coding Required) This works with web apps made on Replit, Bolt, v0, Replit or Cursor. Tech Stack: 1. Claude Desktop (works with ChatGPT) 2. - Builds the Mobile app, Ships to App Store directly from the phone (Uses Claude Code) (Prompt for conversion below) TIMESTAMPS Intro The Web App We Will Convert The Mobile App that Builds Mobile Apps Mobile App Version 1 You can Publish this to the App Store Adding Additional Features (AI APIs) to your app Editing the Mobile App with the Select Feature

How to convert ANY web app into a Mobile App... That can be shipped to the App Store from your phone. (Zero Coding Required) This works with web apps made on Replit, Bolt, v0, Replit or Cursor. Tech Stack: 1. Claude Desktop (works with ChatGPT) 2. - Builds the Mobile app, Ships to App Store directly from the phone (Uses Claude Code) (Prompt for conversion below) TIMESTAMPS Intro The Web App We Will Convert The Mobile App that Builds Mobile Apps Mobile App Version 1 You can Publish this to the App Store Adding Additional Features (AI APIs) to your app Editing the Mobile App with the Select Feature

Riley Brown

59,326 views • 11 months ago

this is unreal f*cking gold for Jev builders 20 repos people are building on Jev right now. browser agents, context tools, trading bots, even a drone 1. JEV-Ultrafast - a fast browser agent ↳ 2. Fast-JEV-Compaction - context compression ↳ 3. JSON-Render - generative UI ↳ 4. Typesafe-MCP - use Jev with any client ↳ 5. JEV-MCP - a judgment toolkit ↳ 6. Semdecide - a classifier that lives in your CLI ↳ 7. JEV-Codex-Router - routes each task to the right model ↳ 8. Winnow - garbage collection for your context ↳ 9. JEV-Review - code review triage ↳ 10. Blink - a repo navigator ↳ 11. Agent-Desktop - desktop automation ↳ 12. Typesafe-Mario - an agent that plays Super Mario ↳ 13. JEV-Drone - drone control ↳ 14. OneVOneJev - a browser FPS ↳ 15. JEV-Trader - HFT market making ↳ 16. Prism - liquidity signal detection ↳ 17. Neo4Jev - knowledge graph traversal ↳ 18. JEV-Curate - training data screening ↳ 19. Canny - checks whether a task was actually completed ↳ 20. KillMyIdea - scores startup ideas before you build them ↳ pick by what you do: > coding -> JEV-Review, Blink, Canny, JEV-Codex-Router > context -> Fast-JEV-Compaction, Winnow > automation -> JEV-Ultrafast, Agent-Desktop > clients and tools -> Typesafe-MCP, JEV-MCP, Semdecide > UI -> JSON-Render > trading -> JEV-Trader, Prism > data -> Neo4Jev, JEV-Curate > founders -> KillMyIdea > just for fun -> Typesafe-Mario, OneVOneJev, JEV-Drone grab the one closest to your job and ship something on top of it this week

this is unreal f*cking gold for Jev builders 20 repos people are building on Jev right now. browser agents, context tools, trading bots, even a drone 1. JEV-Ultrafast - a fast browser agent ↳ 2. Fast-JEV-Compaction - context compression ↳ 3. JSON-Render - generative UI ↳ 4. Typesafe-MCP - use Jev with any client ↳ 5. JEV-MCP - a judgment toolkit ↳ 6. Semdecide - a classifier that lives in your CLI ↳ 7. JEV-Codex-Router - routes each task to the right model ↳ 8. Winnow - garbage collection for your context ↳ 9. JEV-Review - code review triage ↳ 10. Blink - a repo navigator ↳ 11. Agent-Desktop - desktop automation ↳ 12. Typesafe-Mario - an agent that plays Super Mario ↳ 13. JEV-Drone - drone control ↳ 14. OneVOneJev - a browser FPS ↳ 15. JEV-Trader - HFT market making ↳ 16. Prism - liquidity signal detection ↳ 17. Neo4Jev - knowledge graph traversal ↳ 18. JEV-Curate - training data screening ↳ 19. Canny - checks whether a task was actually completed ↳ 20. KillMyIdea - scores startup ideas before you build them ↳ pick by what you do: > coding -> JEV-Review, Blink, Canny, JEV-Codex-Router > context -> Fast-JEV-Compaction, Winnow > automation -> JEV-Ultrafast, Agent-Desktop > clients and tools -> Typesafe-MCP, JEV-MCP, Semdecide > UI -> JSON-Render > trading -> JEV-Trader, Prism > data -> Neo4Jev, JEV-Curate > founders -> KillMyIdea > just for fun -> Typesafe-Mario, OneVOneJev, JEV-Drone grab the one closest to your job and ship something on top of it this week

Mr. Buzzoni

29,333 views • 10 days ago

$JEV + OPUS 5.5 IS INSANE FOR BUILDING A COMPANY BRAIN I pulled the whole architecture out of the TypeSafe and Anthropic docs and packed it into a 14-page PDF the 10 steps: 1. meet the pair > Opus 5.5 thinks, Jev decides, your code holds the branch 2. stop asking a text generator for a yes or no > Jev returns a typed answer with a calibrated probability in 0.44s for $0.00035 3. ask everything at once > Choice, Score and Noul run in parallel, so the fourth question costs almost nothing 4. branch on the number > 0.999 goes straight into the if statement. ~99% of turns end right here 5. stop routing blind > Opus 5.5 to Sonnet and back costs 5.84 against 3.32 for staying on 5.5 6. keep one context warm > cache reads at $0.20 per Mtok are 20x cheaper than a fresh load 7. escalate the hard part > the toughest 1% goes to Opus 5.5 with 1M context and 66.4% on Terminal-Bench 4.0 8. score every chunk on every query > keep whole, summarize or drop. the context gets rebuilt each turn 9. gate the actual command > every bash call gets classified before it runs, inside your own code 10. judge 100% of runs > $3.50 a day for 10,000 traces, and it matched the human label on all 500 decisions the result: a while loop that paid a frontier model for every tiny call turns into a brain that spends a fraction of a cent to notice and pays properly only when it has to think the person who brings this into their team walks into the budget meeting with the AI bill cut and the output up the PDF maps the company brain. the loop side of it - how Jev takes a Claude bill from $765 to $3 a month - is in the article below ↓$

JEV + OPUS 5.5 IS INSANE FOR BUILDING A COMPANY BRAIN I pulled the whole architecture out of the TypeSafe and Anthropic docs and packed it into a 14-page PDF the 10 steps: 1. meet the pair > Opus 5.5 thinks, Jev decides, your code holds the branch 2. stop asking a text generator for a yes or no > Jev returns a typed answer with a calibrated probability in 0.44s for $0.00035 3. ask everything at once > Choice, Score and Noul run in parallel, so the fourth question costs almost nothing 4. branch on the number > 0.999 goes straight into the if statement. ~99% of turns end right here 5. stop routing blind > Opus 5.5 to Sonnet and back costs 5.84 against 3.32 for staying on 5.5 6. keep one context warm > cache reads at $0.20 per Mtok are 20x cheaper than a fresh load 7. escalate the hard part > the toughest 1% goes to Opus 5.5 with 1M context and 66.4% on Terminal-Bench 4.0 8. score every chunk on every query > keep whole, summarize or drop. the context gets rebuilt each turn 9. gate the actual command > every bash call gets classified before it runs, inside your own code 10. judge 100% of runs > $3.50 a day for 10,000 traces, and it matched the human label on all 500 decisions the result: a while loop that paid a frontier model for every tiny call turns into a brain that spends a fraction of a cent to notice and pays properly only when it has to think the person who brings this into their team walks into the budget meeting with the AI bill cut and the output up the PDF maps the company brain. the loop side of it - how Jev takes a Claude bill from $765 to $3 a month - is in the article below ↓

Mr. Buzzoni

85,590 views • 7 days ago